AI 中文总结
本文提出一种用于生物医学信号采集和编码的32通道基于事件的AFE专用集成电路，具有双模编码机制，aADM编码器可实时自适应调整编码数据速率，实现高数据压缩，为脑机接口神经信号自适应无线通信和在线处理提供支持。
AI 中文摘要
低功耗基于事件的模拟前端（AFE）对于构建高效的端到端神经形态信号处理系统至关重要。本文展示了一种针对生物医学信号采集和编码进行优化的基于事件的AFE专用集成电路（ASIC）。该芯片具有32个独立可编程输入通道，采用包括脉冲频率调制（PFM）和自适应异步增量调制器（aADM）电路的双模编码机制输出。aADM编码器提供自动缩放机制，可根据输入信号包络实时调整编码数据速率，实现低功耗信息传输的高数据压缩。此方法为脑机接口中神经信号的自适应无线通信和在线处理铺平道路。该ASIC采用180nm CMOS工艺制造，提供与先进的脉冲神经网络（SNN）神经形态处理器兼容的高度可配置接口。
英文摘要
Low-power event-based Analog Front-Ends (AFEs) are essential for building efficient, end-to-end neuromorphic signal processing systems. In this paper, we present an event-based AFE Application-Specific Integrated Circuit (ASIC) optimized for biomedical signal acquisition and encoding. The chip features 32 independently programmable input channels with dual-mode encoding mechanism outputs, comprising Pulse Frequency Modulation (PFM) and adaptive Asynchronous Delta Modulator (aADM) circuits. The aADM encoder provides an auto-scaling mechanism that adapts the encoding data-rate based on the input signal envelope in real-time, enabling very high data compression for low-power information transmission. This approach paves the way toward adaptive wireless communication of neural signals for on-line processing in brain-computer interfaces. Fabricated in a 180 nm CMOS process, the proposed ASIC offers a highly configurable interface compatible with state-of-the-art Spiking Neural Network (SNN) neuromorphic processors.
CommentsSubmitted to NeuroPHY 2026 workshop, at the EWSN 2026 conference
